Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models

TL;DR

This paper presents a Bayesian Optimization approach using Gaussian processes to efficiently optimize and adapt process parameters in sensor-based sorting systems, reducing experiments and considering uncertainties.

Contribution

It introduces a recurrent optimization method that monitors and adjusts sorting parameters dynamically using Gaussian process surrogate models.

Findings

Reduces the number of experiments needed for optimization

Effectively balances multiple sorting objectives

Incorporates uncertainty into process parameter adjustment

Abstract

Sensor-based sorting systems enable the physical separation of a material stream into two fractions. The sorting decision is based on the image data evaluation of the sensors used and is carried out using actuators. Various process parameters must be set depending on the properties of the material stream, the dimensioning of the system, and the required sorting accuracy. However, continuous verification and re-adjustment are necessary due to changing requirements and material stream compositions. In this paper, we introduce an approach for optimizing, recurrently monitoring and adjusting the process parameters of a sensor-based sorting system. Based on Bayesian Optimization, Gaussian process regression models are used as surrogate models to achieve specific requirements for system behavior with the uncertainties contained therein. This method minimizes the number of necessary experiments while simultaneously considering two possible optimization targets based on the requirements for both material output streams. In addition, uncertainties are considered during determining sorting accuracies in the model calculation. We evaluated the method with three example process parameters.